Free access to paper on causal inference of binary experimental data


  • Author: Statistics Views
  • Date: 11 February 2019

Each week, we select a recently published article and offer either free access or highlight a recent open access publication. This week's is from the Scandinavian Journal of Statistics and is available from the March issue of 2019.

Model‐free causal inference of binary experimental data

Peng Ding and Luke W. Miratrix

Scandinavian Journal of Statistics, Volume 46, Issue 1, March 2019, pages 200-214


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For binary experimental data, we discuss randomization‐based inferential procedures that do not need to invoke any modeling assumptions. In addition to the classical method of moments, we also introduce model‐free likelihood and Bayesian methods based solely on the physical randomization without any hypothetical super population assumptions about the potential outcomes. These estimators have some properties superior to moment‐based ones such as only giving estimates in regions of feasible support. Due to the lack of identification of the causal model, we also propose a sensitivity analysis approach that allows for the characterization of the impact of the association between the potential outcomes on statistical inference.

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